%0 Conference Proceedings
%T Towards the Exploitation of Statistical Language Models for Sentiment Analysis of Twitter Posts
%+ University College of London [London] (UCL)
%+ University of Calcutta
%A Bhattacharya, Sukriti
%A Banerjee, Prasun
%Z Part 4: Engineering of Enterprise Software Products
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 16th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM)
%C Bialystok, Poland
%Y Khalid Saeed
%Y Władysław Homenda
%Y Rituparna Chaki
%I Springer International Publishing
%3 Computer Information Systems and Industrial Management
%V LNCS-10244
%P 253-263
%8 2017-06-16
%D 2017
%R 10.1007/978-3-319-59105-6_22
%K Sentiment analysis
%K N-gram Language Model
%K Perplexity
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X In this paper, we investigate the utility of linguistic features for detecting the sentiment of twitter messages. The sentiment is defined to be a personal positive or negative feelings. We built n-gram language models over zoos of positive and negative tweets. We assert the polarity of a given tweet by observing the perplexity with the positive or negative language model. The given tweet is considered to be close to the language model that assigns lower perplexity.
%G English
%Z TC 8
%2 https://inria.hal.science/hal-01656232/document
%2 https://inria.hal.science/hal-01656232/file/448933_1_En_22_Chapter.pdf
%L hal-01656232
%U https://inria.hal.science/hal-01656232
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC8
%~ IFIP-CISIM
%~ IFIP-LNCS-10244